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High-resolution reconstruction of sparse data from dense low-resolution spatio-temporal data

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2 Author(s)
Qing Yang ; Comput. Res. Div., Lawrence Berkeley Nat. Lab., CA, USA ; Parvin, B.

A novel approach for reconstruction of sparse high-resolution data from lower-resolution dense spatio-temporal data is introduced. The basic idea is to compute the dense feature velocities from lower-resolution data and project them to the corresponding high-resolution data for computing the missing data. In this context, the basic flow equation is solved for intensity, as opposed to feature velocities at high resolution. Although the proposed technique is generic, we have applied our approach to sea surface temperature (SST) data at 18 km (low-resolution dense data) for computing the feature velocities and at 4 km (high-resolution sparse data) for interpolating the missing data. At low resolution, computation of the flow field is regularized and uses the incompressibility constraints for tracking fluid motion. At high resolution, computation of the intensity is regularized for continuity across multiple frames.

Published in:

Image Processing, IEEE Transactions on  (Volume:12 ,  Issue: 6 )